{"title":"Transport scaling in porous media convection","authors":"Xiaojue Zhu, Yifeng Fu, Marco De Paoli","doi":"10.1017/jfm.2024.528","DOIUrl":"https://doi.org/10.1017/jfm.2024.528","url":null,"abstract":"<p><img href=\"S0022112024005287_figAb.png\" mimesubtype=\"png\" mimetype=\"image\" orientation=\"\" position=\"\" src=\"https://static.cambridge.org/content/id/urn%3Acambridge.org%3Aid%3Aarticle%3AS0022112024005287/resource/name/S0022112024005287_figAb.png?pub-status=live\" type=\"\"/></p>","PeriodicalId":15853,"journal":{"name":"Journal of Fluid Mechanics","volume":"7 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yiqing Li, Bernd R. Noack, Tianyu Wang, Guy Y. Cornejo Maceda, Ethan Pickering, Tamir Shaqarin, Artur Tyliszczak
{"title":"Jet mixing enhancement with Bayesian optimization, deep learning and persistent data topology","authors":"Yiqing Li, Bernd R. Noack, Tianyu Wang, Guy Y. Cornejo Maceda, Ethan Pickering, Tamir Shaqarin, Artur Tyliszczak","doi":"10.1017/jfm.2024.525","DOIUrl":"https://doi.org/10.1017/jfm.2024.525","url":null,"abstract":"<p><img href=\"S0022112024005251_figAb.png\" mimesubtype=\"png\" mimetype=\"image\" orientation=\"\" position=\"\" src=\"https://static.cambridge.org/content/id/urn%3Acambridge.org%3Aid%3Aarticle%3AS0022112024005251/resource/name/S0022112024005251_figAb.png?pub-status=live\" type=\"\"/></p>","PeriodicalId":15853,"journal":{"name":"Journal of Fluid Mechanics","volume":"2017 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transition delay in a Mach 6 boundary layer using steady blowing and suction strips","authors":"Christoph Hader, Hermann F. Fasel","doi":"10.1017/jfm.2024.468","DOIUrl":"https://doi.org/10.1017/jfm.2024.468","url":null,"abstract":"<p><img href=\"S0022112024004683_figAb.png\" mimesubtype=\"png\" mimetype=\"image\" orientation=\"\" position=\"\" src=\"https://static.cambridge.org/content/id/urn%3Acambridge.org%3Aid%3Aarticle%3AS0022112024004683/resource/name/S0022112024004683_figAb.png?pub-status=live\" type=\"\"/></p>","PeriodicalId":15853,"journal":{"name":"Journal of Fluid Mechanics","volume":"17 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rahil N. Valani, Brendan Harding, Yvonne M. Stokes
Small finite-size particles suspended in fluid flow through an enclosed curved duct can focus to points or periodic orbits in the two-dimensional duct cross-section. This particle focusing is due to a balance between inertial lift forces arising from axial flow and drag forces arising from cross-sectional vortices. The inertial particle focusing phenomenon has been exploited in various industrial and medical applications to passively separate particles by size using purely hydrodynamic effects. A fixed size particle in a circular duct with a uniform rectangular cross-section can have a variety of particle attractors, such as stable nodes/spirals or limit cycles, depending on the radius of curvature of the duct. Bifurcations occur at different radii of curvature, such as pitchfork, saddle-node and saddle-node infinite period (SNIPER), which result in variations in the location, number and nature of these particle attractors. By using a quasi-steady approximation, we extend the theoretical model of Harding et al. (J. Fluid Mech., vol. 875, 2019, pp. 1–43) developed for the particle dynamics in circular ducts to spiral duct geometries with slowly varying curvature, and numerically explore the particle dynamics within. Bifurcations of particle attractors with respect to radius of curvature can be traversed within spiral ducts and give rise to a rich nonlinear particle dynamics and various types of tipping phenomena, such as bifurcation-induced tipping (B-tipping), rate-induced tipping (R-tipping) and a combination of both, which we explore in detail. We discuss implications of these unsteady dynamical behaviours for particle separation and propose novel mechanisms to separate particles by size in a non-equilibrium manner.
{"title":"Inertial particle focusing in fluid flow through spiral ducts: dynamics, tipping phenomena and particle separation","authors":"Rahil N. Valani, Brendan Harding, Yvonne M. Stokes","doi":"10.1017/jfm.2024.487","DOIUrl":"https://doi.org/10.1017/jfm.2024.487","url":null,"abstract":"Small finite-size particles suspended in fluid flow through an enclosed curved duct can focus to points or periodic orbits in the two-dimensional duct cross-section. This particle focusing is due to a balance between inertial lift forces arising from axial flow and drag forces arising from cross-sectional vortices. The inertial particle focusing phenomenon has been exploited in various industrial and medical applications to passively separate particles by size using purely hydrodynamic effects. A fixed size particle in a circular duct with a uniform rectangular cross-section can have a variety of particle attractors, such as stable nodes/spirals or limit cycles, depending on the radius of curvature of the duct. Bifurcations occur at different radii of curvature, such as pitchfork, saddle-node and saddle-node infinite period (SNIPER), which result in variations in the location, number and nature of these particle attractors. By using a quasi-steady approximation, we extend the theoretical model of Harding <jats:italic>et al.</jats:italic> (<jats:italic>J. Fluid Mech.</jats:italic>, vol. 875, 2019, pp. 1–43) developed for the particle dynamics in circular ducts to spiral duct geometries with slowly varying curvature, and numerically explore the particle dynamics within. Bifurcations of particle attractors with respect to radius of curvature can be traversed within spiral ducts and give rise to a rich nonlinear particle dynamics and various types of tipping phenomena, such as bifurcation-induced tipping (B-tipping), rate-induced tipping (R-tipping) and a combination of both, which we explore in detail. We discuss implications of these unsteady dynamical behaviours for particle separation and propose novel mechanisms to separate particles by size in a non-equilibrium manner.","PeriodicalId":15853,"journal":{"name":"Journal of Fluid Mechanics","volume":"57 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Previous studies have shown that low-frequency vortex oscillations occur around a hemisphere–cylinder body at different angles of attack, but the underlying mechanism is still unclear. In this study, we examine the origin of the vortex oscillation using numerical simulations and global linear stability analysis. The vortex oscillation is reproduced using numerical simulations, and the oscillatory modes are computed through dynamic mode decomposition (DMD). We obtain the base flow through a selective frequency damping method, which exhibits a pair of steady leeward vortices over the body. The four unstable modes are computed using a modified Arnoldi iteration. The antisymmetric mode with a Strouhal number of 0.105 is discovered to be responsible for the alternate oscillation of the vortex pair, and the mode with a Strouhal number of 0.220 corresponds to the in-phase vortex oscillation. Their frequencies have good agreement with the modes of DMD. The other two unstable modes with higher frequencies, one antisymmetric and one symmetric, are harmonic frequencies of the above two modes. The study conclusively verifies that the vortex oscillation over a hemisphere–cylinder body originates from a global flow instability.
{"title":"Mechanism of vortex oscillation around a hemisphere–cylinder body","authors":"Zhou-Yang Wang, Bao-Feng Ma","doi":"10.1017/jfm.2024.526","DOIUrl":"https://doi.org/10.1017/jfm.2024.526","url":null,"abstract":"Previous studies have shown that low-frequency vortex oscillations occur around a hemisphere–cylinder body at different angles of attack, but the underlying mechanism is still unclear. In this study, we examine the origin of the vortex oscillation using numerical simulations and global linear stability analysis. The vortex oscillation is reproduced using numerical simulations, and the oscillatory modes are computed through dynamic mode decomposition (DMD). We obtain the base flow through a selective frequency damping method, which exhibits a pair of steady leeward vortices over the body. The four unstable modes are computed using a modified Arnoldi iteration. The antisymmetric mode with a Strouhal number of 0.105 is discovered to be responsible for the alternate oscillation of the vortex pair, and the mode with a Strouhal number of 0.220 corresponds to the in-phase vortex oscillation. Their frequencies have good agreement with the modes of DMD. The other two unstable modes with higher frequencies, one antisymmetric and one symmetric, are harmonic frequencies of the above two modes. The study conclusively verifies that the vortex oscillation over a hemisphere–cylinder body originates from a global flow instability.","PeriodicalId":15853,"journal":{"name":"Journal of Fluid Mechanics","volume":"2 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haokai Wu, Kai Zhang, Dai Zhou, Wen-Li Chen, Zhaolong Han, Yong Cao
This study proposes a novel super-resolution (or SR) framework for generating high-resolution turbulent boundary layer (TBL) flow from low-resolution inputs. The framework combines a super-resolution generative adversarial neural network (SRGAN) with down-sampling modules (DMs), integrating the residual of the continuity equation into the loss function. The DMs selectively filter out components with excessive energy dissipation in low-resolution fields prior to the super-resolution process. The framework iteratively applies the SRGAN and DM procedure to fully capture the energy cascade of multi-scale flow structures, collectively termed the SRGAN-based energy cascade reconstruction framework (EC-SRGAN). Despite being trained solely on turbulent channel flow data (via ‘zero-shot transfer’), EC-SRGAN exhibits remarkable generalization in predicting TBL small-scale velocity fields, accurately reproducing wavenumber spectra compared to direct numerical simulation (DNS) results. Furthermore, a super-resolution core is trained at a specific super-resolution ratio. By leveraging this pretrained super-resolution core, EC-SRGAN efficiently reconstructs TBL fields at multiple super-resolution ratios from various levels of low-resolution inputs, showcasing strong flexibility. By learning turbulent scale invariance, EC-SRGAN demonstrates robustness across different TBL datasets. These results underscore the potential of EC-SRGAN for generating and predicting wall turbulence with high flexibility, offering promising applications in addressing diverse TBL-related challenges.
{"title":"High-flexibility reconstruction of small-scale motions in wall turbulence using a generalized zero-shot learning","authors":"Haokai Wu, Kai Zhang, Dai Zhou, Wen-Li Chen, Zhaolong Han, Yong Cao","doi":"10.1017/jfm.2024.521","DOIUrl":"https://doi.org/10.1017/jfm.2024.521","url":null,"abstract":"This study proposes a novel super-resolution (or SR) framework for generating high-resolution turbulent boundary layer (TBL) flow from low-resolution inputs. The framework combines a super-resolution generative adversarial neural network (SRGAN) with down-sampling modules (DMs), integrating the residual of the continuity equation into the loss function. The DMs selectively filter out components with excessive energy dissipation in low-resolution fields prior to the super-resolution process. The framework iteratively applies the SRGAN and DM procedure to fully capture the energy cascade of multi-scale flow structures, collectively termed the SRGAN-based energy cascade reconstruction framework (EC-SRGAN). Despite being trained solely on turbulent channel flow data (via ‘zero-shot transfer’), EC-SRGAN exhibits remarkable generalization in predicting TBL small-scale velocity fields, accurately reproducing wavenumber spectra compared to direct numerical simulation (DNS) results. Furthermore, a super-resolution core is trained at a specific super-resolution ratio. By leveraging this pretrained super-resolution core, EC-SRGAN efficiently reconstructs TBL fields at multiple super-resolution ratios from various levels of low-resolution inputs, showcasing strong flexibility. By learning turbulent scale invariance, EC-SRGAN demonstrates robustness across different TBL datasets. These results underscore the potential of EC-SRGAN for generating and predicting wall turbulence with high flexibility, offering promising applications in addressing diverse TBL-related challenges.","PeriodicalId":15853,"journal":{"name":"Journal of Fluid Mechanics","volume":"1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：